Last updated: 2026-03-03
By Rakesh KS — CA Final | Automating Traditional Finance Process with Python & AI | Quant Finance | Algo Trading | Capital Markets |
Gain access to a complete Hawkes-process toolkit for market microstructure, including core maximum likelihood estimation logic, a synthetic flash-crash simulator, and analytics tools to visualize order-flow bursts. Use it to analyze liquidity dynamics, test volatility scenarios, and accelerate research without rebuilding the core components from scratch.
Published: 2026-03-03
Run realistic market-microstructure simulations and visualize order-flow bursts using a ready-to-use Hawkes-process toolkit.
Rakesh KS — CA Final | Automating Traditional Finance Process with Python & AI | Quant Finance | Algo Trading | Capital Markets |
Gain access to a complete Hawkes-process toolkit for market microstructure, including core maximum likelihood estimation logic, a synthetic flash-crash simulator, and analytics tools to visualize order-flow bursts. Use it to analyze liquidity dynamics, test volatility scenarios, and accelerate research without rebuilding the core components from scratch.
Created by Rakesh KS, CA Final | Automating Traditional Finance Process with Python & AI | Quant Finance | Algo Trading | Capital Markets |.
- Quant researchers prototyping market-microstructure hypotheses, - Algo traders testing Hawkes-based volatility models in practice, - Data scientists learning Hawkes processes with a finance focus
Interest in finance for operators. No prior experience required. 1–2 hours per week.
Core MLE algorithm. Synthetic flash-crash simulator. Analytics for order-flow bursts
$1.50.
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